Functional Classification and Pathway Identification
Principal component analysis (PCA) was performed to assess variability for the soybean genotypes under control and drought conditions using the PRCOMP command with default setting in the R software package (Robinsonet al. , 2010). Log2-transformed FPKM values of the DEGs were used for K-means clustering using Pearson correlation in Microarray Experiment Viewer (MeV, v4.9) software. AgriGO v2.0 database (http://systemsbiology.cau.edu.cn/agriGOv2/) was implemented for GO enrichment analysis of DEGs by using Glycine max as reference background, and DEGs were classified into three major categories: biological processes (BP), cellular components (CC) and molecular function (MF) (Tian et al. , 2017). Reduce and Visualize GO analyses /REVIGO (http://revigo.irb.hr/) were performed to remove the redundancy of GO terms using SimRel semantic similarity measure, with an allowed similarity of 0.7 (medium), and the results were displayed as scatter plot (Supek et al. , 2011). Up- and down-regulated transcripts were subjected to MapMan software version 3.6.0 RC1 (http://mapman.gabipd.org/web/guest/mapman) (Usadel et al. , 2009). Mapped gene intensity of fold change of various pathways (both biological or metabolic) were plotted by blue and red schema. The KEGG (Kyoto encyclopedia of gene and genome) was further utilized for pathway enrichment analysis of DEGs (Kanehisa et al. , 2008).